Ready to learn how to train highly accurate, custom AI models – without massive labeled data?
We recommend to join Predibase’s upcoming webinar, Intro to Reinforcement Fine-Tuning: The Future of LLM Customization, on March 27 at 10:00 AM PT.
Reinforcement Fine-Tuning (RFT) redefines traditional Supervised Fine-Tuning by delivering breakthrough performance with as few as 10 labeled examples. Inspired by GRPO (the RL technique behind DeepSeek-R1), RFT uses reward functions for a self-improving, interactive training process—ideal for code generation, advanced RAG, and more.
Why You Should Attend
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Revolutionary RFT Approach: Discover how RFT achieves breakthrough performance and can be used to create a reasoning powerhouse.
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RFT vs. SFT: Learn why RFT outperforms SFT when data is scarce and get a practical framework for deciding which approach fits your use case.
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Real-World Success Story: See how we trained a 32B-param model to write CUDA code 3x faster and more accurately than larger LLMs like OpenAI’s o1 and DeepSeek-R1. This open-sourced model will be shared.
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Live Demo: See RFT in action, from creating reward functions to deploying optimized models on our new, fully managed RFT platform.
Cut the data-labeling bottleneck and harness reinforcement learning to build AI that keeps improving over time.